A monotonic statistical machine translation approach to speaking style transformation

This paper presents a method for automatically transforming faithful transcripts or ASR results into clean transcripts for human consumption using a framework we label speaking style transformation (SST). We perform a detailed analysis of the types of corrections performed by human stenographers when creating clean transcripts, and propose a model that is able to handle the majority of the most common corrections. In particular, the proposed model uses a framework of monotonic statistical machine translation to perform not only the deletion of disfluencies and insertion of punctuation, but also correction of colloquial expressions, insertions of omitted words, and other transformations. We provide a detailed description of the model implementation in the weighted finite state transducer (WFST) framework. An evaluation of the proposed model on both faithful transcripts and speech recognition results of parliamentary and lecture speech demonstrates the effectiveness of the proposed model in performing the wide variety of corrections necessary for creating clean transcripts.

► We present a method for transforming faithful/ASR transcripts to clean transcripts.
► This method is called “speaking style transformation.”
► We perform an analysis of the corrections performed by human stenographers.
► Based on this, we propose a model that is able to handle the most common corrections.
► On parliamentary speech, the system is accurate across many types of transformations.